Sequencing Chicago: Mapping Urban
Metabolism
Jack A Gilbert
@gilbertjacka
www.americangut.org
www.microbial-models.com
www...
400million city dwellers
China will add
221Chinese cities will have 1M
or more people.
And by 2030...
Rapid Urbanization i...
Produced by: S. Jiang, J. Ferreira, M. Gonzalez (2011) | Data Source: CMAP Travel Tracker Data, 2008.
Reference: Jiang, S....
Crowd Funded Human Microbiome – American Gut
4
>$800,000
8450 56
www.americangut.org
House 1 Dynamic Bayesian Network
Predicting Interactions between people and
surfaces
Adding dogs into the mix make the interaction
space more complex.
US-EC Workshop on Marine Genomics: Next Generation Scien...
We can forensically identify physical
connections between people
Young Couple living with a lodger
- you can identify the ...
University of Chicago: Kim Handley, Simon Lax,
Daniel Smith, Kristen Starkey, John Alverdy,
Emily Landon, Jack Gilbert, et...
The Hospital Microbiome shifts towards a human
microbiome following arrival of patients and staff
-3 -2 -1 0 1 2
-2-1012
CCA1
CCA2
-101
F
DO
ALKALINITY
w_102
w_36
w_73
w_96
W_36, W_73
W_112, W_96
Chicago Area Waterways P...
Mapping human and building microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Se...
Mapping human and building microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Se...
Mapping air, water and green-site microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Genera...
Mapping air, water and green-site microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Genera...
Array of Things – Air Microbiome
Temperature
Humidity
Light
Sound
CO2
IR
Motion
Ultrasonic (proximity)
Precipitation
Anemo...
Array of Things – Air Microbiome
Temperature
Humidity
Light
Sound
CO2
IR
Motion
Ultrasonic (proximity)
Precipitation
Anemo...
Current 30 node prototype
A 30-node
prototype is
being
developed for
deployment in
summer 2014
with internal
funding from
...
Business and
Tourism
Dense
Commerci
al
Neighborhood
s and
recreational
corridors
Vision for 2015*
* Funding Permitting
Vision for 2016*
Neighborhood
s and
recreational
corridors
Business and
Tourism
Dense
Commerci
al
* Even More Funding Perm...
Within 5 years: Automated Air Microbiome Detection
Rapid detection of:
• Pathogens
• Microbial imbalance
• Allergens
• Pol...
Predicting the microbiome across all cities
Josh Ladau, Katie Pollard
23
Predicting Historical Changes in the Microbiome:
Facilitating Forecasting
Haiyen Chu, Josh Ladau
Research TeamInvesting Partners
(engineering team)
Charlie Catlett, Rob Jacob, Raj Sankaran,
Cristina Negri, Julian Gordon...
Almaden may 6th 2014 gilbert
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Almaden may 6th 2014 gilbert

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My 10 minute Talk at IBM Almalden's Sequencing the City on the Chicago Sequencing initiative.

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Almaden may 6th 2014 gilbert

  1. 1. Sequencing Chicago: Mapping Urban Metabolism Jack A Gilbert @gilbertjacka www.americangut.org www.microbial-models.com www.homemicrobiome.com www.earthmicrobiome.org www.hospitalmicrobiome.com
  2. 2. 400million city dwellers China will add 221Chinese cities will have 1M or more people. And by 2030... Rapid Urbanization in Developing Economies of Chinese people will live in cities with 1M or more people. In 2025: 70% ....requiring the construction of one New York City every year for several decades Source: Foreign Policy Magazine, Sep/Oct 2010, “Megacities,” Richard Dobbs (McKinsey Global Institute) Landsat images of the Pearl River Delta in 1980 and 2005, illustrating the impact of urbanization on the planet. Between now and 2020, the Guangdong province will invest $229B in 202 ongoing and 258 new transport infrastructure projects to create a single 50M person city.
  3. 3. Produced by: S. Jiang, J. Ferreira, M. Gonzalez (2011) | Data Source: CMAP Travel Tracker Data, 2008. Reference: Jiang, S., J. Ferreira, and M. González. 2012. Clustering Daily Patterns of Human Activities in the City. Data Mining and Knowledge Discovery. Volume 25, Number 3, Pages 478-510 Mapping Megadata for Human Activity Patterns: survey data for 10,000 Chicago households on two weekdays in 2008
  4. 4. Crowd Funded Human Microbiome – American Gut 4 >$800,000 8450 56 www.americangut.org
  5. 5. House 1 Dynamic Bayesian Network Predicting Interactions between people and surfaces
  6. 6. Adding dogs into the mix make the interaction space more complex. US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing House 4 Dynamic Bayesian Network
  7. 7. We can forensically identify physical connections between people Young Couple living with a lodger - you can identify the ‘relationship’ from the microbiome - you can also tell which parts of the house the lodger uses. A young family (parents with 2 young boys) shows no such delineation.
  8. 8. University of Chicago: Kim Handley, Simon Lax, Daniel Smith, Kristen Starkey, John Alverdy, Emily Landon, Jack Gilbert, etc. Illinois Institute of Technology: Tiffanie Ramos, Brent Stephens University of Toronto: Jeff Siegel Building science data summary • 84 variables measured continuously every 5 minutes • 100,000+ data points per variable • 8.4 million+ data points collected • over 8500+ hours of active data collection per variable Microbial Community Analysis • Bacterial, Fungal diversity and function over 12,000 samples • Patients, Staff, Air, Water, Surfaces Patient Records • Age, Sex, disease burden, antibiotics, admission, stay, blood tests, surgery, anesthesia, etc.
  9. 9. The Hospital Microbiome shifts towards a human microbiome following arrival of patients and staff
  10. 10. -3 -2 -1 0 1 2 -2-1012 CCA1 CCA2 -101 F DO ALKALINITY w_102 w_36 w_73 w_96 W_36, W_73 W_112, W_96 Chicago Area Waterways Project 112 36 96 73 0% 10% 20% 30% 40% 50% 60% 70% fish mucus human feces Goose feces Bird associated Cat feces mammal feces animal skin May June july Aug. Sept. May June July Aug. Sept. May Aug. Sept. May June July Aug. Sept. Some samples were dominated by goose, human and animal feca microbiota • City Municipal Water reclamation Department Study • $4M over 7 years • Tracking sources of impact • Tracking impact of water management strategies
  11. 11. Mapping human and building microbiota US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
  12. 12. Mapping human and building microbiota US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing Homes, Offices, Hospitals, Public Restrooms Gyms, Sports Stadiums, Retail
  13. 13. Mapping air, water and green-site microbiota US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
  14. 14. Mapping air, water and green-site microbiota US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing Array of Things
  15. 15. Array of Things – Air Microbiome Temperature Humidity Light Sound CO2 IR Motion Ultrasonic (proximity) Precipitation Anemometer ...
  16. 16. Array of Things – Air Microbiome Temperature Humidity Light Sound CO2 IR Motion Ultrasonic (proximity) Precipitation Anemometer ... Microbial community Temperature Carbon Dioxide Carbon Monoxide NOx Humidity Weather events Wind speed Wind direction Bluetooth signals Visibility Noise level Air quality Air density Local tweet mining
  17. 17. Current 30 node prototype A 30-node prototype is being developed for deployment in summer 2014 with internal funding from Argonne National Laboratory.
  18. 18. Business and Tourism Dense Commerci al Neighborhood s and recreational corridors Vision for 2015* * Funding Permitting
  19. 19. Vision for 2016* Neighborhood s and recreational corridors Business and Tourism Dense Commerci al * Even More Funding Permitting
  20. 20. Within 5 years: Automated Air Microbiome Detection Rapid detection of: • Pathogens • Microbial imbalance • Allergens • Pollution Influence policy: • Urban planning • Threat response • Medical surveillance • Pollution management In all Environments: • Air • Water (rivers, lakes) • Soil (parks, agriculture) • Human bodies
  21. 21. Predicting the microbiome across all cities Josh Ladau, Katie Pollard
  22. 22. 23 Predicting Historical Changes in the Microbiome: Facilitating Forecasting Haiyen Chu, Josh Ladau
  23. 23. Research TeamInvesting Partners (engineering team) Charlie Catlett, Rob Jacob, Raj Sankaran, Cristina Negri, Julian Gordon, Syed Hashsham, Aaron Packman, etc.

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